Full Citation
Title: “Yes We Can!”: A Practical Approach to Teaching Reproducibility to Undergraduates
Citation Type: Journal Article
Publication Year: 2023
ISBN:
ISSN: 2644-2353
DOI: 10.1162/99608F92.9E002F7B
NSFID:
PMCID:
PMID:
Abstract: Is it feasible to include reproducible research methods in undergraduate training in quantitative data analysis? There are reasons to believe the answer to that question is ‘no’—that reproducibility is an advanced topic best left to graduate school or early career training. Professional standards such as the AEA Data Editor’s (2023) guidelines and the World Bank Development Impact Evaluation (DIME) manual (Bjarkefur et al., 2021) may appear too technical and complex to introduce to undergraduates. Even the TIER Protocol (Project TIER, 2023a), which was designed to be accessible to students at all levels, is elaborated with a degree of specificity and detail that could give instructors the impression that incorporating reproducibility into undergraduate classes and research supervision would be a costly and disruptive undertaking. This essay argues that, on the contrary, integrating reproducibility into the undergraduate curriculum is eminently feasible. To support this claim, we present a simple exercise of the kind that might be assigned in an introductory quantitative methods class, and then develop four versions of the exercise: a baseline in which the issue of reproducibility is entirely neglected, and three subsequent versions that incrementally introduce essential elements of reproducibility. The additional skills students must acquire for each version of the exercise are modest, but cumulatively they prepare students in computational methods that achieve state-of-the-art standards of reproducibility. These exercises demonstrate the feasibility of teaching reproducibility to undergraduates, and provide instructors with concrete examples of small, practical steps they can take to achieve that goal.
Url: https://hdsr.mitpress.mit.edu/pub/mza9ii75/release/1
User Submitted?: No
Authors: Ball, Richard
Periodical (Full): Harvard Data Science Review
Issue: 3
Volume: 5
Pages: 1-10
Data Collections: IPUMS USA
Topics: Education, Methodology and Data Collection
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